What is a Structured Data?

Because every field has a known name, type, and position, structured data can be validated, indexed, and queried with predictable, deterministic logic (SQL, typed API responses, spreadsheet columns). This is the opposite end of the spectrum from unstructured data like free-text documents or images, where meaning has to be extracted rather than read directly off a schema. Semi-structured formats like JSON or XML sit in between — they have some organizational markers (keys, tags) but not a rigid, uniform schema across every record. Structured data is what makes traditional automation reliable: a workflow that reads a known field from a known position rarely breaks unexpectedly. The challenge in most real systems is that a large share of business-relevant information — emails, PDFs, chat transcripts — starts unstructured, and turning it into structured data (extraction, classification, schema mapping) is exactly the kind of judgment-heavy task language models are good at, versus rule-based parsers that break on format variation.